AI辅助能提升医学生诊断准确率，且组合使用效果更优。
Predictive AI Can Support Human Learning while Preserving Error Diversity
- 在训练和练习中分别引入AI，可独立提升诊断准确率。
- 同时在训练与练习阶段使用AI，准确率提升幅度超过单一阶段。
- 能保留个体错误多样性，利于团队诊断决策质量。
我们研究了预测性AI在医学新手肺癌诊断训练中的影响。在两项预先注册的实地实验中，我们考察了在诊断训练或练习阶段是否提供AI建议，或两者皆有。结果表明，不同部署方式对专业人士有不同影响：在训练或练习阶段单独使用AI均可提升个体诊断准确率；而同时在两个阶段部署则带来叠加增益，优于单一阶段。此外，在训练与早期练习中使用AI，可提高个体后续独立诊断的准确性。除个体表现外，AI部署还影响个体间错误的多样性，进而影响集体决策的准确性（如第二、第三方会诊时）。
原文摘要 · Abstract (English)
We examined the effects of predictive AI deployment on the immediate performance and learning of medical novices. In two pre-registered field experiments, we varied whether AI input was provided during the training or practice of lung cancer diagnoses, or both. Our results show that different AI deployments have distinct implications for human professionals. AI input during training or practice independently improves individuals' diagnostic accuracy, whereas deployment across both phases yields gains that exceed either approach alone. Furthermore, AI input in both training and earlier practice can improve the accuracy of individuals' subsequent independent diagnoses. Beyond individual accuracy, AI deployment affects the diversity of errors across individuals, with consequences for the accuracy of group decisions (e.g. when getting a second or third opinion on a diagnosis).
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